Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16559
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dc.contributor.authorAlleema, N Noor-
dc.contributor.authorChoudhary, Amar-
dc.contributor.authorRajan, Siddhi Nath-
dc.contributor.authorKancharla, Rakesh-
dc.contributor.authorKothari, Rakshit-
dc.contributor.authorKumar, Rakesh-
dc.date.accessioned2024-08-29T05:42:09Z-
dc.date.available2024-08-29T05:42:09Z-
dc.date.issued2024-
dc.identifier.citationChapter 8; pp. 160-181en_US
dc.identifier.isbn9798369316634-
dc.identifier.isbn9798369316627-
dc.identifier.urihttps://doi.org/10.4018/979-8-3693-1662-7.ch008-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16559-
dc.description.abstractThrough the combination of tool learning patterns, this study offers a novel strategy for personalised treatment for the majority of breast malignancies. The authors used a carefully assembled dataset that included 3444 cases of drug management data, affected person profiles, diagnostic scans, and scientific reviews to train artificial neural networks (ANN), support vector machines (SVM), decision trees (DT), and random forests (RF) for drug sensitivity prediction modelling. While SVM demonstrated its capacity to handle high-dimensional statistics with an accuracy of 96.5%, the artificial neural network (ANN) exhibited remarkable versatility, achieving a commendable accuracy rate of 97.5%. The interpretability inherent in decision trees (DT) and the combined energy of random forests (RF) added crucial elements to the multifaceted methodology. The outcome of the research underscores that the proposed machine learning model stands out with the highest efficacy in predicting the most accurate drug for a given patient. © 2024, IGI Global. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherBlockchain and IoT Approaches for Secure Electronic Health Records (EHR)en_US
dc.publisherIGI Globalen_US
dc.subjectMachine Learningen_US
dc.subjectDrug Sensitivityen_US
dc.subjectBreast Canceren_US
dc.subjectGene Expression Dataen_US
dc.subjectPredictive Modelen_US
dc.titleA Machine Learning-Based Predictive Model for Drug Sensitivity In Breast Cancer Using Gene Expression Dataen_US
dc.typeBook Chapteren_US
Appears in Collections:Book/ Book Chapters

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